Video scene detection method

Abstract

The invention discloses a video scene detection method. According to the method, video data is detected by a computer instead of artificial detection, and scenes in the video are recognized. The detection method includes an offline training discrimination model process and a video scene detection process. The offline training discrimination model process includes: centralizing each video extraction feature comprising semantic and space-time feature extraction aiming to training video samples; performing category annotation of feature vectors and obtaining a group of sample set; performing iteration training of the sample set by employing a multiple-kernel learning frame and obtaining an offline training model. The video scene detection process includes: accessing to a monitoring video source; performing video sampling and obtaining a short video; extracting features from the short video; and loading the offline training model, detecting the features, and obtaining a detection result. According to the method, the scenes in the video can be recognized by the computer instead of artificial detection, the detection efficiency can be improved, the cost is lowered, and convenience is brought to data storage and retrieval.

Description

technical field [0001] The invention relates to video information analysis technology, in particular to a video scene detection method. Background technique [0002] At present, the video surveillance system is becoming more and more popular, and it plays an irreplaceable role in maintaining social order and cracking criminal cases. In the field of video surveillance, it is very important to identify abnormal scenes. For example, accurate detection of behaviors that hinder public safety such as mob fights, and abnormal operations of small businesses and hawkers are of great significance in the fields of social management and urban management. [0003] The video surveillance system includes front-end cameras, transmission equipment and video surveillance platforms. The camera collects the front-end video image signal, and sends it to the monitoring platform after being compressed by the transmission equipment. The platform will complete the work of data storage and abnormal ...

AI Technical Summary

Problems solved by technology

However, the object of the recognition of the scene in the prior art is mainly a static picture

Compared with static pictures, video has a time dimension and contains background change information and target object motion information, so it is more complicated to process

At present, most of the manual methods are used to monitor, process and find abnormal scenes in the video data, which is time-consuming, labor-intensive, costly, and inefficient, and the accuracy cannot be guaranteed, and it is difficult to efficiently realize the analysis of video processing and analysis result data. Storage and later retrieval for reuse

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